At the beginning, MMD utilizes one-hot encoding to select user \(u\)  and item \(i\) , then projects them into \(p_u\)  and \(q_i\) . Latent factor model (LFM), which is the most widely used model in recommender area [1]}, is employed to get \(p_u\)  and \(q_i\) , and calculates rating score \(s^r\)  with them. Meanwhile, we employ HDAN to project the review \(v_{ui}\)  to a sentiment score \(s^v\) . Note that we use ratings \(r_{ui}\)  as training ground truth for LFM and HDAN. Then we feed \(s^v\)  and \(s^r\)  into the professional malicious user profiling model (MUP). This model outputs the sentiment gap vector \(g_{ui}\)  and labels professional malicious users to build a candidate set \(U^\text{cmu}\) . We combine \(g_{ui}\)  and \(p_u\)  to build a profile vector \(z_u\)  and utilize \(U^\text{cmu}\)  as ground truth to learn a proper metric matrix \(A_\text{L}\)  for professional malicious user detection. Especially, we apply attention to metric learning to enhance the model. Finally, we choose \(U^\text{cmu}\)  as cluster centroids, \(A_\text{L}\)  as the clustering metric to cluster the users, which achieves the professional malicious users \(U^\text{mu}\) . The details of MMD are introduced in the following subsections.
